Introduction

This a template for an analysis notebook using RMarkdown.

In this notebook, we will set up parameters in the seurat workflow [normalization –> reduction –> clustering] for one Wilms tumor sample (SCPCS000169) of the Wilms Tumor dataset (SCPCP000006).

This correspond to the step 2 of the proposed analysis: clustering of cells across a set of parameters for few samples

In order to assess the clustering quality, we look into some marker genes, pathways enrichment and label transfer.

Sample metadata

## to be modified when rendered
filedir <- filelist[i] 
sample <- gsub(paste0(data_dir, "/"), "", filedir)

metadata <- read.table(file.path(data_dir, "/single_cell_metadata.tsv"), sep = "\t", header=TRUE)

metadata[metadata$scpca_sample_id == sample,]
scpca_project_id scpca_sample_id scpca_library_id diagnosis subdiagnosis disease_timing age_at_diagnosis sex tissue_location participant_id submitter submitter_id organism development_stage_ontology_term_id sex_ontology_term_id organism_ontology_id self_reported_ethnicity_ontology_term_id disease_ontology_term_id tissue_ontology_term_id metastasis relapse_status treatment vital_status seq_unit technology total_reads mapped_reads sample_cell_count_estimate unfiltered_cells filtered_cell_count processed_cells has_cellhash includes_anndata is_cell_line is_multiplexed is_xenograft pi_name project_title genome_assembly mapping_index alevin_fry_version salmon_version transcript_type droplet_filtering_method cell_filtering_method prob_compromised_cutoff min_gene_cutoff normalization_method date_processed workflow workflow_version workflow_commit
3 SCPCP000006 SCPCS000170 SCPCL000207 Wilms tumor Favorable Initial diagnosis 3.5 F Kidney SJWLM071556 murphy_chen SJWLM071556_D1 Homo sapiens HsapDv:0000097 PATO:0000383 NCBITaxon:9606 unknown MONDO:0006058 UBERON:0002113 NA No Upfront resection Alive nucleus 10Xv3.1 194036441 136641781 33536 72320 33536 26532 False True False False False murphy_chen Single nuclear RNA-seq and spatial transcriptomic analysis of anaplastic and favorable histology Wilms tumor Homo_sapiens.GRCh38.104 Homo_sapiens.GRCh38.104.spliced_intron.txome 0.7.0 1.5.2 [total, spliced] emptyDropsCellRanger miQC 0.75 200 deconvolution 2024-03-18T20:20:15+0000 https://github.com/AlexsLemonade/scpca-nf v0.8.0 8bef82d853d19e5aeddd75401aa54cf8bfbced13

Description of the analysis workflow

We perform the following analysis to assess for the quality of clustering:

[1] We perform some quality check to assess any QC-induced clustering (nFeature, nCount, percent.mito).

[2] We add cell cycle information, as we know that in a specific cell cycle state, the transcriptional program is mostly/exclusively related to cell cycle genes and the identity of cells is difficult to determine. We expect these cells to cluster together in a cluster of proliferating cells.

[3] We look at specific marker genes that we reported in the table marker.sets/CellType_metadata.csv to check the relevance of the clustering.

[4] We look at specific pathways that we reported in the table marker.sets/Pathways_metadata.csv to check the relevance of the clustering.

[5] We run DElegate::FindAllMarkers2 to find markers of the different clusters and manually check if they do make sense. DElegate::FindAllMarkers2 is an improved version of Seurat::FindAllMarkers based on pseudobulk differential expression method.

[6] We perform enrichment analysis of marker genes for each seurat clusters. We defined all the genes from the seurat object as the universe and used the MSigDB gene sets.

[7] We plot pca/umap reduction grouping with available annotations (singler_, cellassign_). We expect at least immune cells to be correctly label and fall into a few set of clusters.

[ ] Next step, aim for a next PR: We will label transfer (Azimuth) to transfer annotation from the fetal kidney atlas human reference. We plot pca/umap reduction grouping with latest labels. We expect it to be the most representative of the cell types in the sample.

Selected parameters

Please note: to keep the notebook as straight as possible, we decided to show the analysis for the selected set of parameters:

  • SCTransform (default parameters)
  • RunPCA (default)
  • RunUMAP (dims = 1:50)
  • FindNeighbors (dims = 1:50)
  • FindClusters (default)

Note: Other parameters have been previously tested, but we would like to show in the following report that the one selected is performing good.

Analysis content

Load the processed SingleCellExperiment rds object

file <- list.files(filedir)
file <- file[grepl("_processed.rds", file)]

# open the processed rds object
sce <- readRDS(paste0(filedir, "/", file))

Run Seurat pipeline

# convert to seurat
srat <- CreateSeuratObject(counts = counts(sce),
                                    assay = "RNA",
                                    project = sample
                           )

# convert colData and rowData to data.frame for use in the Seurat object
cell_metadata <- as.data.frame(colData(sce))
row_metadata <- as.data.frame(rowData(sce))

# add cell metadata (colData) from SingleCellExperiment to Seurat
srat@meta.data <- cell_metadata

# add row metadata (rowData) from SingleCellExperiment to Seurat
srat[["RNA"]]@meta.data <- row_metadata

# add metadata from SingleCellExperiment to Seurat
srat@misc <- metadata(sce)


# Normalization
options(future.globals.maxSize= 8912896000000)
srat <- SCTransform(srat, verbose = F, conserve.memory = TRUE)

# dimensionality reduction
srat <- RunPCA(srat, verbose = F)
srat <- RunUMAP(srat, dims = 1:50, verbose = F)

# clustering
srat    <- FindNeighbors(srat, dims = 1:50, verbose = F)
srat    <- FindClusters(srat, verbose = T)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 26532
## Number of edges: 821246
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8020
## Number of communities: 14
## Elapsed time: 4 seconds

Visualize seurat clusters

d2 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - umap")
d1 <- SCpubr::do_DimPlot(srat, reduction="pca", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - pca")
v <- SCpubr::do_ViolinPlot(srat, features = c( "subsets_mito_percent"), ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(2,2,4))

We expect up to 5 set of clusters:

  • blastema cancer cells
  • epithelium cancer and/or normal cells
  • stroma cancer and/or normal cells
  • immune cells
  • endothelial cells

Cell cycle information

s.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$s.genes]
g2m.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$g2m.genes]

srat <- CellCycleScoring(srat, s.features = s.genes, g2m.features = g2m.genes, set.ident = FALSE)
d2 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "Phase", label = TRUE) + ggtitle("Phase - umap")
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - umap")

b <- SCpubr::do_BarPlot(sample = srat,
                         group.by = "Phase",
                         split.by = "seurat_clusters",
                         position = "fill",
                         font.size = 10,
                         legend.ncol = 3) +
                         ggtitle("% cells")+
                         xlab(file)
d1 + d2 + b + plot_layout(ncol = 3, widths = c(2,2,4))

 SCpubr::do_ViolinPlot(srat, features = c("S.Score", "G2M.Score"), ncol = 2, group.by = "seurat_clusters", legend.position = "none")

Look at specific genes

Here, we open the table of marker genes marker-sets/CellType_metadata.csv. Note: we do not expect to have a clear and nice pattern of expression for all of the following markers in every tumor. This is just ti get a few idea.

DT::datatable(CellType_metadata, caption = ("CellType_metadata"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',

                               buttons = c( 'csv', 'excel')))

Malignant markers

 SCpubr::do_ViolinPlot(srat, features = rownames(srat)[rownames(srat) %in% (CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class %in% c("malignant")])] , ncol = 9, group.by = "seurat_clusters", legend.position = "none")

NCAM1
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000149294", pt.size = 0.2) + ggtitle("NCAM1")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000149294", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

COL6A3
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000163359", pt.size = 0.2) + ggtitle("COL6A3")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000163359", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

IGF2
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000167244", pt.size = 0.2) + ggtitle("IGF2")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000167244", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

WT1
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000184937", pt.size = 0.2) + ggtitle("WT1")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000184937", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

EPCAM
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000119888", pt.size = 0.2) + ggtitle("EPCAM")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000119888", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

Immune markers

 SCpubr::do_ViolinPlot(srat, features = rownames(srat)[rownames(srat) %in% (CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class %in% c("immune")])]
                       , ncol = 6, group.by = "seurat_clusters", legend.position = "none")

PTPRC = CD45
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000081237", pt.size = 0.2) + ggtitle("PTPRC = CD45")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000081237", ncol = 1, group.by = "seurat_clusters", legend.position = "none", pt.size = 2) 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

Other markers

 SCpubr::do_ViolinPlot(srat, features = rownames(srat)[rownames(srat) %in% (CellType_metadata$ENSEMBL_ID[! CellType_metadata$cell_class %in% c("immune", "malignant")])]
                       , ncol = 6, group.by = "seurat_clusters", legend.position = "none")

VWF, endothelial cells
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000110799", pt.size = 0.2) + ggtitle("VWF")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000110799", ncol = 1, group.by = "seurat_clusters", legend.position = "none", pt.size = 2) 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

Look at specific pathways

Here, we open the table of marker genes marker-sets/Pathway_metadata.csv.

DT::datatable(Pathway_metadata, caption = ("Pathway_metadata"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',

                               buttons = c( 'csv', 'excel')))

TP53 pathway

here we will calculate a TP53 score using AddMduleScore and the genes of the HALLMARK_P53_PATHWAY gene set.

## define genesets
hallmarks <- msigdbr(species = "human", category = "H")

TP53_list = hallmarks %>% 
  filter(gs_name == "HALLMARK_P53_PATHWAY") %>%
  dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()

TP53_list <- list(TP53_list$human_ensembl_gene)

srat <- AddModuleScore(srat, features = TP53_list, name = "TP53_score")
## Warning: The following features are not present in the object: ENSG00000147889, ENSG00000147883, ENSG00000137975, ENSG00000284841, ENSG00000276536,
## ENSG00000206377, ENSG00000206478, ENSG00000227231, ENSG00000230128, ENSG00000235030, ENSG00000237155, ENSG00000163083, ENSG00000128422, ENSG00000177551,
## ENSG00000175793, ENSG00000206297, ENSG00000224212, ENSG00000224748, ENSG00000226173, ENSG00000227816, ENSG00000230705, ENSG00000232367, ENSG00000125657, not
## searching for symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "TP53_score1", pt.size = 0.2) + ggtitle("TP53 score")
v <- SCpubr::do_ViolinPlot(srat, features = "TP53_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

“Normal” cells (immune, endothelial) have a slightly higher TP53 score.

DNA repair pathway

here we will calculate a DNA_repair score using AddMduleScore and the genes of the HALLMARK_DNA_REPAIR gene set.

DNA_repair = hallmarks %>% 
  filter(gs_name == "HALLMARK_DNA_REPAIR") %>%
  dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()

DNA_repair_list <- list(DNA_repair$human_ensembl_gene)

srat <- AddModuleScore(srat, features = DNA_repair_list, name = "DNA_repair_score")
## Warning: The following features are not present in the object: ENSG00000284752, ENSG00000288114, ENSG00000206268, ENSG00000206357, ENSG00000229363,
## ENSG00000231044, ENSG00000233801, ENSG00000180099, ENSG00000206502, ENSG00000224859, ENSG00000233795, ENSG00000235176, ENSG00000235443, ENSG00000236808,
## ENSG00000236949, ENSG00000284832, ENSG00000280627, ENSG00000276463, ENSG00000285339, ENSG00000274352, not searching for symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "DNA_repair_score1", pt.size = 0.2) + ggtitle("DNA_repair score")
v <- SCpubr::do_ViolinPlot(srat, features = "DNA_repair_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

Note: Chemo-treated samples should have higher DNA-damage scores.

DROSHA target genes

## define genesets
c3 <- msigdbr(species = "human", category = "C3", subcategory = "TFT:GTRD")

DROSHA_list = c3 %>% 
  filter(gs_name == "DROSHA_TARGET_GENES") %>%
  dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()

DROSHA_list <- list(DROSHA_list$human_ensembl_gene)

srat <- AddModuleScore(srat, features = DROSHA_list, name = "DROSHA_score")
## Warning: The following features are not present in the object: ENSG00000265134, ENSG00000210164, ENSG00000210100, ENSG00000210156, ENSG00000209082,
## ENSG00000210112, ENSG00000210107, ENSG00000210151, ENSG00000210077, ENSG00000248923, ENSG00000199568, ENSG00000207205, ENSG00000275538, not searching for
## symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "DROSHA_score1", pt.size = 0.2) + ggtitle("DROSHA score")
v <- SCpubr::do_ViolinPlot(srat, features = "DROSHA_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

DICER1 target genes

DICER1_list = c3 %>% 
  filter(gs_name == "DICER1_TARGET_GENES") %>%
  dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()

DICER1_list <- list(DICER1_list$human_ensembl_gene)

srat <- AddModuleScore(srat, features = DICER1_list, name = "DICER1_score")
## Warning: The following features are not present in the object: ENSG00000210049, ENSG00000210100, ENSG00000210156, ENSG00000210196, ENSG00000210151,
## ENSG00000210195, ENSG00000222328, ENSG00000199347, not searching for symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "DICER1_score1", pt.size = 0.2) + ggtitle("DICER1 score")
v <- SCpubr::do_ViolinPlot(srat, features = "DICER1_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none") 

d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))

Find marker genes for each of the seurat clusters

In addition to the list of known marker genes, we used an unbiased approach to find transcripts that characterized the different clusters. We run DElegate::FindAllMarkers2 to find markers of the different clusters and manually check if they do make sense. DElegate::FindAllMarkers2 is an improved version of Seurat::FindAllMarkers based on pseudobulk differential expression method. Please check the preprint from Chistoph Hafemeister: https://www.biorxiv.org/content/10.1101/2023.03.28.534443v1 and tool described here: https://github.com/cancerbits/DElegate

feature_conversion <- srat@assays$RNA@meta.data
de_results   <- DElegate::FindAllMarkers2(srat, group_column = "seurat_clusters")

#filter the most relevant markers
s.markers <- de_results[de_results$padj < cfg$padj_thershold & de_results$log_fc > cfg$lfc_threshold & de_results$rate1 > cfg$rate1_threshold,]

# add gene symbol for easiest interpretation of the result
s.markers$gene_ids <- s.markers$feature
s.markers <- left_join(s.markers,feature_conversion, by = c( "gene_ids") )
identical(s.markers$feature, s.markers$gene_ids)     # check the quality of the merge, must be true
## [1] TRUE
DT::datatable(s.markers, caption = ("marker genes"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',

                               buttons = c( 'csv', 'excel')))
# Select top 5 genes for heatmap plotting
s.markers <- na.omit(s.markers)
s.markers %>%
    group_by(group1) %>%
    top_n(n =  5, wt = log_fc) -> top5

# subset for plotting
cells <- WhichCells(srat, downsample = 100)
ss <- subset(srat, cells = cells)
ss <- ScaleData(ss, features = top5$feature)

p1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
p2 <- DoHeatmap(ss, features = top5$feature,  cells = cells, group.by = "seurat_clusters") + NoLegend() + 
  scale_fill_gradientn(colors =  c("#01665e","#35978f",'darkslategray3', "#f7f7f7", "#fee391","#fec44f","#F9AD03")) 
p3 <- ggplot(srat@meta.data, aes(seurat_clusters, fill = seurat_clusters)) + geom_bar() + NoLegend()


common_title <- sprintf("Unsupervised clustering %s, %d cells", srat@meta.data$orig.ident[1], ncol(srat))
show((((p1 / p3) + plot_layout(heights = c(3,2)) | p2) ) + plot_layout(widths = c(1, 2)) + plot_layout(heights = c(3,1)) + plot_annotation(title = common_title))

DT::datatable(top5[, c(1, 9, 11, 12)], caption = ("top 5 marker genes"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',

                               buttons = c( 'csv', 'excel')))

Enrichment analysis of marker genes

Here we perform enrichment analysis of the marker genes found in the previous section for each Seurat cluster.

We defined as universe/background all the genes expressed in the dataset, meaning the rownames of the Seurat object.

We used three gene sets from MSiGDB:

  • the hallmark gene sets are coherently expressed signatures derived by aggregating many MSigDB gene sets to represent well-defined biological states or processes.
  • the C3 : regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites.
  • the C8 : cell type signature gene sets curated from cluster markers identified in single-cell sequencing studies of human tissue.

We used enricher function from clusterProfiler to perform enrichment analysis.

# define background genes = universe for enrichment
background <-  row_metadata$gene_ids

## define genesets
c8 <- msigdbr(species = "human", category = "C8")

msigdbr_hallmarks = hallmarks %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()
msigdbr_c3 = c3 %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()
msigdbr_c8 = c8 %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()

Hallmarks MSiGDB gene sets

tmp_H <- NULL
for(i in unique(srat$seurat_clusters)){
signature = s.markers$feature[s.markers$group1 == i]
ego_module <- enricher(gene = signature, universe = background, TERM2GENE = msigdbr_hallmarks)
tmp <- NULL

  if(!is.null(ego_module)){
    if(dim(ego_module)[1]>0){
    b_H <- barplot(ego_module, showCategory = 15, font.size = 20, label_format = 40)  
    tmp <- b_H$data
    tmp$set <- "Hallmarks"
    tmp$cluster <- i
    }
  }
tmp_H <- rbind(tmp_H, tmp)
}



ggplot(tmp_H, aes(Count, ID)) +
    geom_bar(stat = "identity", aes(fill=cluster))+

  theme_classic()+ 
    geom_text(
        aes(label = (paste( "Padj = ", round(p.adjust,2)))),
        color = "black",
        size = 3,
        hjust=1,
        position = position_dodge(0.5)
    )+
   theme(text = element_text(size=14))+
  facet_wrap(facets=c("cluster"), ncol = length(unique(tmp_H$cluster)))

EMT signature should be enriched in stroma cluster. E2F/proliferation should be enriched in blastema cluster. MYC(N), TP53 must be enriched in blastema cluster.

C3 MSiGDB regulatory target gene sets

tmp_H <- NULL
for(i in unique(srat$seurat_clusters)){
signature = s.markers$feature[s.markers$group1 == i]
ego_module <- enricher(gene = signature, universe = background, TERM2GENE = msigdbr_c3)
tmp <- NULL

  if(!is.null(ego_module)){
    if(dim(ego_module)[1]>0){
    b_H <- barplot(ego_module, showCategory = 15, font.size = 20, label_format = 40)  
    tmp <- b_H$data
    tmp$ID <- factor(tmp$ID, levels = c(tmp$ID[order(tmp$Count)]))
    tmp$set <- "c8"
    tmp$cluster <- i
    }
  }
tmp_H <- rbind(tmp_H, tmp[1:5,])
}



ggplot(tmp_H, aes(Count, ID)) +
    geom_bar(stat = "identity", aes(fill=cluster))+

  theme_classic()+ 
    geom_text(
        aes(label = (paste( "Padj = ", round(p.adjust,2)))),
        color = "black",
        size = 3,
        hjust=1,
        position = position_dodge(0.5)
    )+
   theme(text = element_text(size=14))+
  facet_wrap(facets=c("cluster"), ncol = length(unique(tmp_H$cluster)))
## Warning: Removed 7 rows containing missing values or values outside the scale range (`geom_bar()`).
## Warning: Removed 7 rows containing missing values or values outside the scale range (`geom_text()`).

Here to check of we catch and MIR pattern (DROSHA; DICER1; other?)

C8 MSiGDB cell type signature gene sets

tmp_H <- NULL
for(i in unique(srat$seurat_clusters)){
signature = s.markers$feature[s.markers$group1 == i]
ego_module <- enricher(gene = signature, universe = background, TERM2GENE = msigdbr_c8)
tmp <- NULL

  if(!is.null(ego_module)){
    if(dim(ego_module)[1]>0){
    b_H <- barplot(ego_module, showCategory = 15, font.size = 20, label_format = 40)  
    tmp <- b_H$data
    tmp$ID <- factor(tmp$ID, levels = c(tmp$ID[order(tmp$Count)]))
    tmp$set <- "c8"
    tmp$cluster <- i
    }
  }
tmp_H <- rbind(tmp_H, tmp[1:5,])
}



ggplot(tmp_H, aes(Count, ID)) +
    geom_bar(stat = "identity", aes(fill=cluster))+

  theme_classic()+ 
    geom_text(
        aes(label = (paste( "Padj = ", round(p.adjust,2)))),
        color = "black",
        size = 3,
        hjust=1,
        position = position_dodge(0.5)
    )+
   theme(text = element_text(size=14))+
  facet_wrap(facets=c("cluster"), ncol = length(unique(tmp_H$cluster)))
## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_bar()`).
## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).

The MSigDB C8 gene set is quite relevant for kidney and nephroblastoma annotations. Epithelial (cancer and normal) cells should be enriched in mature/adulte kidney pathways while blastema cancer cells will show enrichment of fetal kidney development pathway / cap mesenchyme.

Annotation from SingleR (no cancer or kidney specific dataset)

Here, we quickly checked annotations that are present in the _processed rds object. However, the automated annotation have not been performed using a cancer specific reference or a kidney reference. We do not expect a nice labelling of the cells as the overlap of cell types between the reference and the query dataset is poor. This support the need to do a proper label transfer from the fetal kidney atlas, which is imho the best reference that can be applied to a Wilms tumor query.

d2 <-DimPlot(srat, group.by = "singler_celltype_annotation", reduction = "umap", label = TRUE, repel = TRUE) + NoLegend()



DT::datatable(table(srat$seurat_clusters, srat$singler_celltype_annotation), caption = ("table of SingleR annotation per seurat clusters"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',

                               buttons = c( 'csv', 'excel')))
d3 <- SCpubr::do_BarPlot(sample = srat,
                         group.by = "singler_celltype_annotation",
                         split.by = "seurat_clusters",
                         position = "fill",
                         font.size = 10,
                         legend.ncol = 3) +
                         ggtitle("% cells")+
                         xlab(file)

d2|d3
## Warning: ggrepel: 13 unlabeled data points (too many overlaps). Consider increasing max.overlaps

Annotation from cellassign (no cancer or kidney specific dataset)

d2 <-DimPlot(srat, group.by = "cellassign_celltype_annotation", reduction = "umap", label = TRUE, repel = TRUE) + NoLegend()


DT::datatable(table(srat$seurat_clusters, srat$cellassign_celltype_annotation)
, caption = ("table of cellassign annotation per seurat clusters"), 
              extensions = 'Buttons', 
              options = list(  dom = 'Bfrtip',

                               buttons = c( 'csv', 'excel')))
d3 <- SCpubr::do_BarPlot(sample = srat,
                         group.by = "cellassign_celltype_annotation",
                         split.by = "seurat_clusters",
                         position = "fill",
                         font.size = 10,
                         legend.ncol = 3) +
                         ggtitle("% cells")+
                         xlab(file)

d2|d3

Even if the result of SingleR and CellAssign are not specific to a Wilms tumor dataset, it give the first impression that C12 is a cluster of endothelial cells and C11 a cluster of immune cells.

Azimuth annotation from fetal kidney

To be added in a next PR.

For more information related to the reference, please go to https://www.kidneycellatlas.org/ You will find:

  • interactive viewer

  • h5ad files to download.

Please note that as Wilms tumor have been described to be closer to fetal kidney as mature kidney, we only used the fetal kidney atlas as the reference. Also check : https://www.science.org/doi/10.1126/science.aat5031

This part is imho one of the most important step that allow us to have a quick and reliable idea of the composition of the different clusters. The predicted compartment are defined into 4 categories:

  • endotheliúm
  • stroma
  • fetal nephron
  • immune

As for SingleR and CellAssign, the annotation of immune cells and endothelial cells is straightforward. The stroma compartment should then contain normal and cancer stromal cells. The fetal nephron compartment contain blastema cancer cells a well as normal and cancer epithelial cells.

Further segregation of cancer versus normal cells will be achieved using a combination of markers/pathways (see above) and inferred CNV (to be done).

Next step

-[x] We will adapt this template to be render on all the 40 Wilms tumor samples of the dataset.

-[ ] We will save for each sample the rds file

-[ ] We will run inferCNV for each sample to decide the malignant/normal status of some stroma and epithelial cluster, and confirm the blastema annotation.

The next step will provide us a better understanding of the entire cohort. We will then have to set up a strategy to annotate each sample. Open questions are:

[ ] should we annotate single cell
or
[] consider applying similar annotations to all cells in a cluster?

[ ] manual annotation of each cluster / each patient
or
[] automated annotation using some threshold?

Session Info

# record the versions of the packages used in this analysis and other environment information
sessionInfo()
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
##  [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Vienna
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] assertthat_0.2.1            data.table_1.15.4           clusterProfiler_4.12.2      msigdbr_7.5.1               DElegate_1.2.1             
##  [6] edgeR_4.2.1                 limma_3.60.4                viridis_0.6.5               viridisLite_0.4.2           SCpubr_2.0.2               
## [11] ggplotify_0.1.2             patchwork_1.2.0             ggplot2_3.5.1               DT_0.33                     dplyr_1.1.4                
## [16] tidyr_1.3.1                 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0              GenomicRanges_1.56.1       
## [21] GenomeInfoDb_1.40.1         IRanges_2.38.1              S4Vectors_0.42.1            BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
## [26] matrixStats_1.3.0           Seurat_5.1.0                SeuratObject_5.0.2          sp_2.1-4                    reticulate_1.38.0          
## [31] Azimuth_0.5.0               shinyBS_0.61.1             
## 
## loaded via a namespace (and not attached):
##   [1] R.methodsS3_1.8.2                 poweRlaw_0.80.0                   goftest_1.2-3                     Biostrings_2.72.1                
##   [5] vctrs_0.6.5                       spatstat.random_3.3-1             digest_0.6.36                     png_0.1-8                        
##   [9] ggrepel_0.9.5                     deldir_2.0-4                      parallelly_1.38.0                 renv_1.0.7                       
##  [13] MASS_7.3-61                       Signac_1.13.0                     reshape2_1.4.4                    httpuv_1.6.15                    
##  [17] qvalue_2.36.0                     withr_3.0.1                       xfun_0.46                         ggfun_0.1.5                      
##  [21] survival_3.7-0                    EnsDb.Hsapiens.v86_2.99.0         memoise_2.0.1                     gson_0.1.0                       
##  [25] tidytree_0.4.6                    zoo_1.8-12                        gtools_3.9.5                      pbapply_1.7-2                    
##  [29] R.oo_1.26.0                       KEGGREST_1.44.1                   promises_1.3.0                    httr_1.4.7                       
##  [33] restfulr_0.0.15                   globals_0.16.3                    fitdistrplus_1.2-1                rhdf5filters_1.16.0              
##  [37] rhdf5_2.48.0                      rstudioapi_0.16.0                 UCSC.utils_1.0.0                  miniUI_0.1.1.1                   
##  [41] generics_0.1.3                    DOSE_3.30.2                       babelgene_22.9                    curl_5.2.1                       
##  [45] zlibbioc_1.50.0                   ggraph_2.2.1                      polyclip_1.10-7                   GenomeInfoDbData_1.2.12          
##  [49] SparseArray_1.4.8                 xtable_1.8-4                      stringr_1.5.1                     pracma_2.4.4                     
##  [53] evaluate_0.24.0                   S4Arrays_1.4.1                    hms_1.1.3                         irlba_2.3.5.1                    
##  [57] colorspace_2.1-1                  hdf5r_1.3.11                      ROCR_1.0-11                       spatstat.data_3.1-2              
##  [61] magrittr_2.0.3                    lmtest_0.9-40                     readr_2.1.5                       glmGamPoi_1.16.0                 
##  [65] later_1.3.2                       ggtree_3.12.0                     lattice_0.22-6                    spatstat.geom_3.3-2              
##  [69] future.apply_1.11.2               shadowtext_0.1.4                  scattermore_1.2                   XML_3.99-0.17                    
##  [73] cowplot_1.1.3                     RcppAnnoy_0.0.22                  pillar_1.9.0                      nlme_3.1-165                     
##  [77] pwalign_1.0.0                     caTools_1.18.2                    compiler_4.4.1                    RSpectra_0.16-2                  
##  [81] stringi_1.8.4                     tensor_1.5                        GenomicAlignments_1.40.0          plyr_1.8.9                       
##  [85] crayon_1.5.3                      abind_1.4-5                       BiocIO_1.14.0                     gridGraphics_0.5-1               
##  [89] googledrive_2.1.1                 locfit_1.5-9.10                   graphlayouts_1.1.1                bit_4.0.5                        
##  [93] fastmatch_1.1-4                   codetools_0.2-20                  crosstalk_1.2.1                   bslib_0.8.0                      
##  [97] SeuratData_0.2.2.9001             plotly_4.10.4                     mime_0.12                         splines_4.4.1                    
## [101] Rcpp_1.0.13                       fastDummies_1.7.3                 sparseMatrixStats_1.16.0          HDO.db_0.99.1                    
## [105] cellranger_1.1.0                  knitr_1.48                        blob_1.2.4                        utf8_1.2.4                       
## [109] here_1.0.1                        seqLogo_1.70.0                    AnnotationFilter_1.28.0           fs_1.6.4                         
## [113] listenv_0.9.1                     DelayedMatrixStats_1.26.0         tibble_3.2.1                      Matrix_1.7-0                     
## [117] statmod_1.5.0                     tzdb_0.4.0                        tweenr_2.0.3                      pkgconfig_2.0.3                  
## [121] tools_4.4.1                       cachem_1.1.0                      RSQLite_2.3.7                     DBI_1.2.3                        
## [125] fastmap_1.2.0                     rmarkdown_2.27                    scales_1.3.0                      grid_4.4.1                       
## [129] ica_1.0-3                         shinydashboard_0.7.2              Rsamtools_2.20.0                  sass_0.4.9                       
## [133] BiocManager_1.30.23               dotCall64_1.1-1                   RANN_2.6.1                        farver_2.1.2                     
## [137] scatterpie_0.2.3                  tidygraph_1.3.1                   yaml_2.3.10                       rtracklayer_1.64.0               
## [141] cli_3.6.3                         purrr_1.0.2                       leiden_0.4.3.1                    lifecycle_1.0.4                  
## [145] uwot_0.2.2                        presto_1.0.0                      BSgenome.Hsapiens.UCSC.hg38_1.4.5 BiocParallel_1.38.0              
## [149] annotate_1.82.0                   gtable_0.3.5                      rjson_0.2.21                      ggridges_0.5.6                   
## [153] progressr_0.14.0                  ape_5.8                           parallel_4.4.1                    jsonlite_1.8.8                   
## [157] RcppHNSW_0.6.0                    TFBSTools_1.42.0                  bitops_1.0-8                      bit64_4.0.5                      
## [161] Rtsne_0.17                        yulab.utils_0.1.5                 spatstat.utils_3.0-5              CNEr_1.40.0                      
## [165] highr_0.11                        jquerylib_0.1.4                   GOSemSim_2.30.0                   shinyjs_2.1.0                    
## [169] SeuratDisk_0.0.0.9021             spatstat.univar_3.0-0             R.utils_2.12.3                    lazyeval_0.2.2                   
## [173] shiny_1.9.1                       htmltools_0.5.8.1                 enrichplot_1.24.2                 GO.db_3.19.1                     
## [177] sctransform_0.4.1                 rappdirs_0.3.3                    ensembldb_2.28.0                  glue_1.7.0                       
## [181] TFMPvalue_0.0.9                   spam_2.10-0                       googlesheets4_1.1.1               XVector_0.44.0                   
## [185] RCurl_1.98-1.16                   treeio_1.28.0                     rprojroot_2.0.4                   BSgenome_1.72.0                  
## [189] gridExtra_2.3                     JASPAR2020_0.99.10                igraph_2.0.3                      R6_2.5.1                         
## [193] labeling_0.4.3                    forcats_1.0.0                     RcppRoll_0.3.1                    GenomicFeatures_1.56.0           
## [197] cluster_2.1.6                     Rhdf5lib_1.26.0                   gargle_1.5.2                      aplot_0.2.3                      
## [201] DirichletMultinomial_1.46.0       DelayedArray_0.30.1               tidyselect_1.2.1                  ProtGenerics_1.36.0              
## [205] ggforce_0.4.2                     AnnotationDbi_1.66.0              future_1.34.0                     munsell_0.5.1                    
## [209] KernSmooth_2.23-24                htmlwidgets_1.6.4                 fgsea_1.30.0                      RColorBrewer_1.1-3               
## [213] rlang_1.1.4                       spatstat.sparse_3.1-0             spatstat.explore_3.3-1            fansi_1.0.6